library(psych)
library(ggpubr)
library(tidyverse)
library(lmerTest)

Introduction

King 2019 Atlas

We will use regions 1 and 2 as control regions since there are related to motor processing. For ROIs we will use regions 5, 6, 7, 8, 9, and 10 since they all overall with crus I and II which have been the main regions implicated in social cogntion. There are also some studies which point to crus II’s involvement in reinforcement learning.

participants <- read.table(file='../participants_good.tsv', sep = '\t', header = TRUE)
participants
all_sub_roi_con <- read.csv('../univariate_roi/all_sub_roi_contrasts.csv')
all_sub_roi_con$subject_id <- all_sub_roi_con$subj
#all_sub_roi_con <- subset(all_sub_roi_con, select = -c(subj))

# Use "adult as group label
all_sub_roi_con$group[all_sub_roi_con$group == 'control'] <- 'college'

all_sub_roi_con

Use all participants as a continuous age cohort

hyp_rois <- c('TPJ_b-social','ATL_b-social','AMG_r-social','PCC_b-social','dmPFC_b-social',
              'vmPFC_b-reward',
              'region01','region02','region05','region06','region07',
              'region08','region09','region10', 
              'striatum_ventral','striatum_dorsala','striatum_dorsalp')

hyp_rois_names <- c('TPJ','ATL','AMG (r)','PCC','dmPFC',
                    'vmPFC',
                    'CB Region 1','CB Region 2','CB Region 5',
                    'CB Region 6','CB Region 7','CB Region 8','CB Region 9','CB Region 10',
                    'Ventral Striatum','Anterior \n Dorsal Striatum','Posterior \n Dorsal Striatum')

all_sub_roi_con$roi <- factor(all_sub_roi_con$roi, levels=hyp_rois)
all_tstats_combined <- data.frame(matrix(ncol=6,nrow=0))
colnames(all_tstats_combined) <- c('task','contrast','roi','t_stat','p.adj','estimate')

tasks <- c('mdoors','social')
contrasts <- c('positive_winVlos', 'all_winVlos')


row_n = 1
for (task in tasks) {
  # Filter for task data
  
  task_data <- all_sub_roi_con[all_sub_roi_con$task %in% task, ]
  
  for (contrast in contrasts) {
    # Filter contrast data
    contrast_data <- task_data[task_data$contrast %in% contrast, ]
    
    for (roi in hyp_rois) {
      # Filter for ROI data
      roi_data <- contrast_data[contrast_data$roi %in% roi, ]
      
      # Calculate t-test
      temp_ttest <- t.test(roi_data$contrast_mean, mu=0)
      
      # Fill in dataframe
      all_tstats_combined[row_n,] <- c(task,contrast,roi,
                                 temp_ttest$statistic, temp_ttest$p.value,
                                 temp_ttest$estimate)
      
      # Increase counter for row numbers
      
      row_n = row_n+1
    }
  }
}

Change numbers to numeric

all_tstats_combined <- transform(all_tstats_combined, t_stat = as.numeric(t_stat),
                                          p = as.numeric(p.adj),
                                          estimate = as.numeric(estimate))

Positive Wins vs Loses

Young Adults

adult_data <- all_sub_roi_con[all_sub_roi_con$group %in% 'college', ]
adult_data_pos <- adult_data[adult_data$contrast %in% 'positive_winVlos', ]
adult_data_pos <- adult_data_pos[adult_data_pos$roi %in% hyp_rois, ]

#allg_data$contrast_mean <- allg_data$contrast_mean * -1
adult_data_pos_mdoors <- adult_data_pos[adult_data_pos$task %in% 'mdoors', ]
adult_data_pos_social <- adult_data_pos[adult_data_pos$task %in% 'social', ]


#allg_data$contrast_mean <- allg_data$contrast_mean * -1

Monetary Task

adult_data_pos_mdoors.t_tests = adult_data_pos_mdoors %>%
                                group_by(roi) %>%
                                summarise(P = t.test(contrast_mean, mu = 0)$p.value,
                                          Sig = ifelse(P < 0.05, "*", ""),
                                          MaxWidth = max(contrast_mean))


stats <- p.adjust(adult_data_pos_mdoors.t_tests$P,method='fdr')
P_adjust <- stats
Sig_adjust = ifelse(P_adjust < 0.005, "***", ifelse(P_adjust < 0.01, "**", 
                                             ifelse(P_adjust < 0.05, "*", "")))

adult_data_pos_mdoors.t_tests$Sig_adjust <- Sig_adjust

p <- ggbarplot(adult_data_pos_mdoors, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               position = position_dodge(0.8))
p + geom_text(aes(label = Sig_adjust, y = 0.3), size = 6,
              data = adult_data_pos_mdoors.t_tests) + 
  theme(axis.text.x = element_text(angle =90, vjust = 0.5)) + 
  xlab("Regions of Interests") + ylab("Mean Response (positive wins > losses)") + 
  scale_x_discrete(labels=hyp_rois_names)

ggsave('../univariate_roi/adult_pos_winVlos_ttest_bar_mdoors.png')

Social Task

adult_data_pos_social.t_tests = adult_data_pos_social %>%
    group_by(roi) %>%
    summarise(P = t.test(contrast_mean, mu = 0)$p.value,
              Sig = ifelse(P < 0.05, "*", ""),
              MaxWidth = max(contrast_mean))


stats <- p.adjust(adult_data_pos_social.t_tests$P,method='fdr')
P_adjust <- stats
Sig_adjust = ifelse(P_adjust < 0.005, "***", ifelse(P_adjust < 0.01, "**", 
                                             ifelse(P_adjust < 0.05, "*", "")))
adult_data_pos_social.t_tests$Sig_adjust <- Sig_adjust 

p <- ggbarplot(adult_data_pos_social, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               position = position_dodge(0.8))
p + geom_text(aes(label = Sig_adjust, y = 0.3), size = 6,
              data = adult_data_pos_social.t_tests) + 
  theme(axis.text.x = element_text(angle =90, vjust = 0.5)) + 
  xlab("Regions of Interests") + ylab("Mean Response (positive wins > losses)") + 
  scale_x_discrete(labels=hyp_rois_names)

ggsave('../univariate_roi/adult_pos_winVlos_ttest_bar_social.png')

Adolescents

adole_data <- all_sub_roi_con[all_sub_roi_con$group %in% 'kid', ]
adole_data_pos <- adole_data[adole_data$contrast %in% 'positive_winVlos', ]
adole_data_pos <- adole_data_pos[adole_data_pos$roi %in% hyp_rois, ]

#allg_data$contrast_mean <- allg_data$contrast_mean * -1
adole_data_pos_mdoors <- adole_data_pos[adole_data_pos$task %in% 'mdoors', ]
adole_data_pos_social <- adole_data_pos[adole_data_pos$task %in% 'social', ]


#allg_data$contrast_mean <- allg_data$contrast_mean * -1

Monetary Task

adole_data_pos_mdoors.t_tests = adole_data_pos_mdoors %>%
    group_by(roi) %>%
    summarise(P = t.test(contrast_mean, mu = 0)$p.value,
              Sig = ifelse(P < 0.05, "*", ""),
              MaxWidth = max(contrast_mean))


stats <- p.adjust(adole_data_pos_mdoors.t_tests$P,method='fdr')
P_adjust <- stats
Sig_adjust = ifelse(P_adjust < 0.005, "***", ifelse(P_adjust < 0.01, "**", 
                                             ifelse(P_adjust < 0.05, "*", "")))
adole_data_pos_mdoors.t_tests$Sig_adjust <- Sig_adjust

p <- ggbarplot(adole_data_pos_mdoors, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               position = position_dodge(0.8))
p + geom_text(aes(label = Sig_adjust, y = 0.3), size = 6,
              data = adole_data_pos_mdoors.t_tests) + 
  theme(axis.text.x = element_text(angle =90, vjust = 0.5)) + 
  xlab("Regions of Interests") + ylab("Mean Response (positive wins > losses)") + 
  scale_x_discrete(labels=hyp_rois_names)

ggsave('../univariate_roi/adole_pos_winVlos_ttest_bar_mdoors.png')

Social Task

adole_data_pos_social.t_tests = adole_data_pos_social %>%
    group_by(roi) %>%
    summarise(P = t.test(contrast_mean, mu = 0)$p.value,
              Sig = ifelse(P < 0.05, "*", ""),
              MaxWidth = max(contrast_mean))


stats <- p.adjust(adole_data_pos_social.t_tests$P,method='fdr')
P_adjust <- stats
Sig_adjust = ifelse(P_adjust < 0.005, "***", ifelse(P_adjust < 0.01, "**", 
                                             ifelse(P_adjust < 0.05, "*", "")))
adole_data_pos_social.t_tests$Sig_adjust <- Sig_adjust

p <- ggbarplot(adole_data_pos_social, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               position = position_dodge(0.8))
p + geom_text(aes(label = Sig_adjust, y = 0.3), size = 6,
              data = adole_data_pos_social.t_tests) + 
  theme(axis.text.x = element_text(angle =90, vjust = 0.5)) + 
  xlab("Regions of Interests") + ylab("Mean Response (positive wins > losses)") + 
  scale_x_discrete(labels=hyp_rois_names)

ggsave('../univariate_roi/adole_pos_winVlos_ttest_bar_social.png')

Group Differences

allg_data_pos <- all_sub_roi_con[all_sub_roi_con$contrast %in% 'positive_winVlos', ]
allg_data_pos <- allg_data_pos[allg_data_pos$roi %in% hyp_rois, ]

#allg_data$contrast_mean <- allg_data$contrast_mean * -1

Monetary Task

allg_data_pos_mdoors <- allg_data_pos[allg_data_pos$task %in% 'mdoors', ]
model_allg_pos_mdoors <- aov(contrast_mean ~ group*roi,
                           data = allg_data_pos_mdoors)
summary(model_allg_pos_mdoors)
##               Df Sum Sq Mean Sq F value Pr(>F)
## group          1   0.01 0.00743   0.097  0.755
## roi           16   1.56 0.09771   1.280  0.202
## group:roi     16   1.16 0.07246   0.949  0.512
## Residuals   1003  76.59 0.07636
allg_data_pos_mdoors.t_tests = allg_data_pos_mdoors %>%
    group_by(roi) %>%
    summarise(P = t.test(contrast_mean ~ group, mu = 0)$p.value,
              Sig = ifelse(P < 0.05, "*", ""),
              MaxWidth = max(contrast_mean))


stats <- p.adjust(allg_data_pos_mdoors.t_tests$P, method='fdr')
P_adjust <- stats
Sig_adjust = ifelse(P_adjust < 0.005, "***", ifelse(P_adjust < 0.01, "**", 
                                             ifelse(P_adjust < 0.05, "*", "")))
allg_data_pos_mdoors.t_tests$Sig_adjust <- Sig_adjust

p <- ggbarplot(allg_data_pos_mdoors, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               color = 'group', position = position_dodge(0.8))
p + geom_text(aes(label = Sig_adjust, y = 0.3), size = 6,
              data = allg_data_pos_mdoors.t_tests) + 
  theme(axis.text.x = element_text(angle =90, vjust = 0.5)) + 
  xlab("Regions of Interests") + ylab("Mean Response (positive wins > losses)") + 
  scale_x_discrete(labels=hyp_rois_names)

#p + stat_compare_means(aes(group = group), label = 'p.signif', label.y=0.3) + 
#  theme(axis.text.x = element_text(angle =45, vjust = 0.5)) + 
#  scale_x_discrete(labels=hyp_rois_names)

ggsave('../univariate_roi/allp_pos_winVlos_anova_bar_mdoors.png')
p <- ggbarplot(allg_data_pos_mdoors, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               color = 'group', position = position_dodge(0.8))
p + stat_compare_means(aes(group = group), label = 'p.signif', label.y=0.3) + 
  theme(axis.text.x = element_text(angle =45, vjust = 0.5)) + 
  scale_x_discrete(labels=hyp_rois_names)

ggsave('../univariate_roi/allp_pos_winVlos_anova_bar_mdoors.png')

Social Task

allg_data_pos_social <- allg_data_pos[allg_data_pos$task %in% 'social', ]
model_allg_pos_social <- aov(contrast_mean ~ group*roi,
                           data = allg_data_pos_social)
summary(model_allg_pos_social)
##               Df Sum Sq Mean Sq F value  Pr(>F)   
## group          1   0.50  0.5035   7.921 0.00498 **
## roi           16   1.89  0.1183   1.862 0.02045 * 
## group:roi     16   0.51  0.0320   0.503 0.94649   
## Residuals   1003  63.76  0.0636                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
allg_data_pos_social.t_tests = allg_data_pos_social %>%
    group_by(roi) %>%
    summarise(P = t.test(contrast_mean ~ group, mu = 0)$p.value,
              Sig = ifelse(P < 0.05, "*", ""),
              MaxWidth = max(contrast_mean))


stats <- p.adjust(allg_data_pos_social.t_tests$P, method='fdr')
P_adjust <- stats
Sig_adjust = ifelse(P_adjust < 0.005, "***", ifelse(P_adjust < 0.01, "**", 
                                             ifelse(P_adjust < 0.05, "*", "")))
allg_data_pos_social.t_tests$Sig_adjust <- Sig_adjust

p <- ggbarplot(allg_data_pos_social, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               color = 'group', position = position_dodge(0.8))
p + geom_text(aes(label = Sig_adjust, y = 0.3), size = 6,
              data = allg_data_pos_social.t_tests) + 
  theme(axis.text.x = element_text(angle =90, vjust = 0.5)) + 
  xlab("Regions of Interests") + ylab("Mean Response (positive wins > losses)") + 
  scale_x_discrete(labels=hyp_rois_names)

#p + stat_compare_means(aes(group = group), label = 'p.signif', label.y=0.3) + 
#  theme(axis.text.x = element_text(angle =45, vjust = 0.5)) + 
#  scale_x_discrete(labels=hyp_rois_names)

ggsave('../univariate_roi/allp_pos_winVlos_anova_bar_social.png')
p <- ggbarplot(allg_data_pos_social, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               color = 'group', position = position_dodge(0.8))
p + stat_compare_means(aes(group = group), label = 'p.signif', label.y=0.3) + 
  theme(axis.text.x = element_text(angle =45, vjust = 0.5))

#ggsave('../univariate_roi/allp_pos_winVlos_anova_bar_social.png')

All Wins vs Loses

Young Adults

adult_data <- all_sub_roi_con[all_sub_roi_con$group %in% 'college', ]
adult_data_all <- adult_data[adult_data$contrast %in% 'all_winVlos', ]
adult_data_all <- adult_data_all[adult_data_all$roi %in% hyp_rois, ]

#allg_data$contrast_mean <- allg_data$contrast_mean * -1
adult_data_all_mdoors <- adult_data_all[adult_data_all$task %in% 'mdoors', ]
adult_data_all_social <- adult_data_all[adult_data_all$task %in% 'social', ]


#allg_data$contrast_mean <- allg_data$contrast_mean * -1

Monetary Task

adult_data_all_mdoors.t_tests = adult_data_all_mdoors %>%
    group_by(roi) %>%
    summarise(P = t.test(contrast_mean, mu = 0)$p.value,
              Sig = ifelse(P < 0.05, "*", ""),
              MaxWidth = max(contrast_mean))


stats <- p.adjust(adult_data_all_mdoors.t_tests$P,method='fdr')
P_adjust <- stats
Sig_adjust = ifelse(P_adjust < 0.005, "***", ifelse(P_adjust < 0.01, "**", 
                                             ifelse(P_adjust < 0.05, "*", "")))
adult_data_all_mdoors.t_tests$Sig_adjust <- Sig_adjust

p <- ggbarplot(adult_data_all_mdoors, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               position = position_dodge(0.8))
p + geom_text(aes(label = Sig_adjust, y = 0.3), size = 6,
              data = adult_data_all_mdoors.t_tests) + 
  theme(axis.text.x = element_text(angle =90, vjust = 0.5)) + 
  xlab("Regions of Interests") + ylab("Mean Response (all wins > losses)") + 
  scale_x_discrete(labels=hyp_rois_names)

ggsave('../univariate_roi/adult_all_winVlos_ttest_bar_mdoors.png')

Social Task

adult_data_all_mdoors.t_tests = adult_data_all_mdoors %>%
    group_by(roi) %>%
    summarise(P = t.test(contrast_mean, mu = 0)$p.value,
              Sig = ifelse(P < 0.05, "*", ""),
              MaxWidth = max(contrast_mean))


stats <- p.adjust(adult_data_all_mdoors.t_tests$P,method='fdr')
P_adjust <- stats
Sig_adjust = ifelse(P_adjust < 0.005, "***", ifelse(P_adjust < 0.01, "**", 
                                             ifelse(P_adjust < 0.05, "*", "")))
adult_data_all_mdoors.t_tests$Sig_adjust <- Sig_adjust

p <- ggbarplot(adult_data_all_mdoors, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               position = position_dodge(0.8))
p + geom_text(aes(label = Sig_adjust, y = 0.3), size = 6,
              data = adult_data_all_mdoors.t_tests) + 
  theme(axis.text.x = element_text(angle =90, vjust = 0.5)) + 
  xlab("Regions of Interests") + ylab("Mean Response (all wins > losses)") + 
  scale_x_discrete(labels=hyp_rois_names)

ggsave('../univariate_roi/adult_all_winVlos_ttest_bar_mdoors.png')
adult_data_all_social.t_tests = adult_data_all_social %>%
    group_by(roi) %>%
    summarise(P = t.test(contrast_mean, mu = 0)$p.value,
              Sig = ifelse(P < 0.05, "*", ""),
              MaxWidth = max(contrast_mean))


stats <- p.adjust(adult_data_all_social.t_tests$P,method='fdr')
P_adjust <- stats
Sig_adjust = ifelse(P_adjust < 0.005, "***", ifelse(P_adjust < 0.01, "**", 
                                             ifelse(P_adjust < 0.05, "*", "")))
adult_data_all_social.t_tests$Sig_adjust <- Sig_adjust

p <- ggbarplot(adult_data_all_social, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               position = position_dodge(0.8))
p + geom_text(aes(label = Sig_adjust, y = 0.3), size = 6,
              data = adult_data_all_social.t_tests) + 
  theme(axis.text.x = element_text(angle =90, vjust = 0.5)) + 
  xlab("Regions of Interests") + ylab("Mean Response (all wins > losses)") + 
  scale_x_discrete(labels=hyp_rois_names)

ggsave('../univariate_roi/adult_all_winVlos_ttest_bar_social.png')

Adolescents

adole_data <- all_sub_roi_con[all_sub_roi_con$group %in% 'kid', ]
adole_data_all <- adole_data[adole_data$contrast %in% 'all_winVlos', ]
adole_data_all <- adole_data_all[adole_data_all$roi %in% hyp_rois, ]
adole_data_all_mdoors <- adole_data_all[adole_data_all$task %in% 'mdoors', ]
adole_data_all_social <- adole_data_all[adole_data_all$task %in% 'social', ]

Monetary Task

adole_data_all_mdoors.t_tests = adole_data_all_mdoors %>%
    group_by(roi) %>%
    summarise(P = t.test(contrast_mean, mu = 0)$p.value,
              Sig = ifelse(P < 0.05, "*", ""),
              MaxWidth = max(contrast_mean))


stats <- p.adjust(adole_data_all_mdoors.t_tests$P,method='fdr')
P_adjust <- stats
Sig_adjust = ifelse(P_adjust < 0.005, "***", ifelse(P_adjust < 0.01, "**", 
                                             ifelse(P_adjust < 0.05, "*", "")))
adole_data_all_mdoors.t_tests$Sig_adjust <- Sig_adjust

p <- ggbarplot(adole_data_all_mdoors, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               position = position_dodge(0.8))
p + geom_text(aes(label = Sig_adjust, y = 0.3), size = 6,
              data = adole_data_all_mdoors.t_tests) + 
  theme(axis.text.x = element_text(angle =90, vjust = 0.5)) + 
  xlab("Regions of Interests") + ylab("Mean Response (all wins > losses)") + 
  scale_x_discrete(labels=hyp_rois_names)

ggsave('../univariate_roi/adole_all_winVlos_ttest_bar_mdoors.png')

Social Task

adole_data_all_social.t_tests = adole_data_all_social %>%
    group_by(roi) %>%
    summarise(P = t.test(contrast_mean, mu = 0)$p.value,
              Sig = ifelse(P < 0.05, "*", ""),
              MaxWidth = max(contrast_mean))


stats <- p.adjust(adole_data_all_social.t_tests$P,method='fdr')
P_adjust <- stats
Sig_adjust = ifelse(P_adjust < 0.005, "***", ifelse(P_adjust < 0.01, "**", 
                                             ifelse(P_adjust < 0.05, "*", "")))
adole_data_all_social.t_tests$Sig_adjust <- Sig_adjust

p <- ggbarplot(adole_data_all_social, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               position = position_dodge(0.8))
p + geom_text(aes(label = Sig_adjust, y = 0.3), size = 6,
              data = adole_data_all_social.t_tests) + 
  theme(axis.text.x = element_text(angle =90, vjust = 0.5)) + 
  xlab("Regions of Interests") + ylab("Mean Response (all wins > losses)") + 
  scale_x_discrete(labels=hyp_rois_names)

ggsave('../univariate_roi/adole_all_winVlos_ttest_bar_social.png')

Group Differences

allg_data_all <- all_sub_roi_con[all_sub_roi_con$contrast %in% 'all_winVlos', ]
allg_data_all <- allg_data_all[allg_data_all$roi %in% hyp_rois, ]

#allg_data$contrast_mean <- allg_data$contrast_mean * -1

Monetary Task

allg_data_all_mdoors <- allg_data_all[allg_data_all$task %in% 'mdoors', ]
model_allg_all_mdoors <- aov(contrast_mean ~ group*roi,
                           data = allg_data_all_mdoors)
summary(model_allg_all_mdoors)
##               Df Sum Sq Mean Sq F value Pr(>F)
## group          1   0.07 0.06990   0.815  0.367
## roi           16   0.85 0.05288   0.616  0.873
## group:roi     16   1.28 0.07986   0.931  0.533
## Residuals   1003  86.05 0.08580
allg_data_all_mdoors.t_tests = allg_data_all_mdoors %>%
    group_by(roi) %>%
    summarise(P = t.test(contrast_mean ~ group, mu = 0)$p.value,
              Sig = ifelse(P < 0.05, "*", ""),
              MaxWidth = max(contrast_mean))


stats <- p.adjust(allg_data_all_mdoors.t_tests$P, method='fdr')
P_adjust <- stats
Sig_adjust = ifelse(P_adjust < 0.005, "***", ifelse(P_adjust < 0.01, "**", 
                                             ifelse(P_adjust < 0.05, "*", "")))
allg_data_all_mdoors.t_tests$Sig_adjust <- Sig_adjust

p <- ggbarplot(allg_data_all_mdoors, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               color = 'group', position = position_dodge(0.8))
p + geom_text(aes(label = Sig_adjust, y = 0.3), size = 6,
              data = allg_data_all_mdoors.t_tests) + 
  theme(axis.text.x = element_text(angle =90, vjust = 0.5)) + 
  xlab("Regions of Interests") + ylab("Mean Response (positive wins > losses)") + 
  scale_x_discrete(labels=hyp_rois_names)

#p + stat_compare_means(aes(group = group), label = 'p.signif', label.y=0.3) + 
#  theme(axis.text.x = element_text(angle =45, vjust = 0.5)) + 
#  scale_x_discrete(labels=hyp_rois_names)

ggsave('../univariate_roi/allp_all_winVlos_anova_bar_mdoors.png')

Social Task

allg_data_all_social <- allg_data_all[allg_data_all$task %in% 'social', ]
model_allg_all_social <- aov(contrast_mean ~ group*roi,
                           data = allg_data_all_social)
summary(model_allg_all_social)
##               Df Sum Sq Mean Sq F value   Pr(>F)    
## group          1   0.90  0.9011  14.057 0.000188 ***
## roi           16   1.76  0.1101   1.718 0.038252 *  
## group:roi     16   0.17  0.0109   0.170 0.999908    
## Residuals   1003  64.29  0.0641                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
allg_data_all_social.t_tests = allg_data_all_social %>%
    group_by(roi) %>%
    summarise(P = t.test(contrast_mean ~ group, mu = 0)$p.value,
              Sig = ifelse(P < 0.05, "*", ""),
              MaxWidth = max(contrast_mean))


stats <- p.adjust(allg_data_all_social.t_tests$P, method='fdr')
P_adjust <- stats
Sig_adjust = ifelse(P_adjust < 0.005, "***", ifelse(P_adjust < 0.01, "**", 
                                             ifelse(P_adjust < 0.05, "*", "")))
allg_data_all_social.t_tests$Sig_adjust <- Sig_adjust

p <- ggbarplot(allg_data_all_social, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               color = 'group', position = position_dodge(0.8))
p + geom_text(aes(label = Sig_adjust, y = 0.3), size = 6,
              data = allg_data_all_social.t_tests) + 
  theme(axis.text.x = element_text(angle =90, vjust = 0.5)) + 
  xlab("Regions of Interests") + ylab("Mean Response (positive wins > losses)") + 
  scale_x_discrete(labels=hyp_rois_names)

#p + stat_compare_means(aes(group = group), label = 'p.signif', label.y=0.3) + 
#  theme(axis.text.x = element_text(angle =45, vjust = 0.5)) + 
#  scale_x_discrete(labels=hyp_rois_names)

ggsave('../univariate_roi/allp_all_winVlos_anova_bar_social.png')
p <- ggbarplot(allg_data_all_social, x = 'roi', 
               y = 'contrast_mean', add = 'mean_se',
               color = 'group', position = position_dodge(0.8))
p + stat_compare_means(aes(group = group), label = 'p.signif', label.y=0.3) + 
  theme(axis.text.x = element_text(angle =45, vjust = 0.5))

ggsave('../univariate_roi/allp_all_winVlos_anova_bar_social.png')

Group x Task x ROI Analysis

Positive Wins vs Losses

allg_data_pos <- all_sub_roi_con[all_sub_roi_con$contrast %in% 'positive_winVlos', ]
allg_data_pos <- allg_data_pos[allg_data_pos$roi %in% hyp_rois, ]
model_allg_pos <- aov(contrast_mean ~ group*task*roi,
                           data = allg_data_pos)
summary(model_allg_pos)
##                  Df Sum Sq Mean Sq F value   Pr(>F)    
## group             1   0.32  0.3166   4.526 0.033509 *  
## task              1   0.65  0.6497   9.286 0.002339 ** 
## roi              16   2.94  0.1837   2.626 0.000433 ***
## group:task        1   0.19  0.1943   2.777 0.095778 .  
## group:roi        16   0.76  0.0476   0.681 0.815591    
## task:roi         16   0.52  0.0323   0.462 0.964770    
## group:task:roi   16   0.91  0.0568   0.812 0.672752    
## Residuals      2006 140.35  0.0700                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
p <- ggplot(data = allg_data_pos, aes(x=roi, y=contrast_mean, fill=task, alpha=group))
p + geom_bar(stat = 'summary', fun = 'mean', position=position_dodge()) +
  scale_alpha_manual(values = c(0.3, 1)) + 
  theme_classic() + 
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

ggsave('../univariate_roi/allp_pos_winVlos_anova_bar.png')

All Wins vs Losses

allg_data_all <- all_sub_roi_con[all_sub_roi_con$contrast %in% 'all_winVlos', ]
allg_data_all <- allg_data_all[allg_data_all$roi %in% hyp_rois, ]
model_allg_all <- aov(contrast_mean ~ group*task*roi,
                           data = allg_data_all)
summary(model_allg_all)
##                  Df Sum Sq Mean Sq F value   Pr(>F)    
## group             1   0.23  0.2345   3.129  0.07706 .  
## task              1   2.57  2.5675  34.257 5.63e-09 ***
## roi              16   2.23  0.1393   1.858  0.02010 *  
## group:task        1   0.74  0.7364   9.826  0.00175 ** 
## group:roi        16   0.76  0.0473   0.631  0.86084    
## task:roi         16   0.38  0.0237   0.316  0.99538    
## group:task:roi   16   0.70  0.0435   0.580  0.90098    
## Residuals      2006 150.35  0.0749                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
p <- ggplot(data = allg_data_all, aes(x=roi, y=contrast_mean, fill=task, alpha=group))
p + geom_bar(stat = 'summary', fun = 'mean', position=position_dodge()) +
  scale_alpha_manual(values = c(0.3, 1)) + 
  theme_classic() + 
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

ggsave('../univariate_roi/allp_all_winVlos_anova_bar.png')